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#include "precomp.hpp"
namespace cv { namespace ml {
ParamGrid::ParamGrid() { minVal = maxVal = 0.; logStep = 1; }
ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
{
minVal = std::min(_minVal, _maxVal);
maxVal = std::max(_minVal, _maxVal);
logStep = std::max(_logStep, 1.);
}
bool StatModel::empty() const { return !isTrained(); }
int StatModel::getVarCount() const { return 0; }
bool StatModel::train( const Ptr<TrainData>&, int )
{
CV_Error(CV_StsNotImplemented, "");
return false;
}
bool StatModel::train( InputArray samples, int layout, InputArray responses )
{
return train(TrainData::create(samples, layout, responses));
}
float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArray _resp ) const
{
Mat samples = data->getSamples();
int layout = data->getLayout();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
const int* sidx_ptr = sidx.ptr<int>();
int i, n = (int)sidx.total();
bool isclassifier = isClassifier();
Mat responses = data->getResponses();
if( n == 0 )
n = data->getNSamples();
if( n == 0 )
return -FLT_MAX;
Mat resp;
if( _resp.needed() )
resp.create(n, 1, CV_32F);
double err = 0;
for( i = 0; i < n; i++ )
{
int si = sidx_ptr ? sidx_ptr[i] : i;
Mat sample = layout == ROW_SAMPLE ? samples.row(si) : samples.col(si);
float val = predict(sample);
float val0 = responses.at<float>(si);
if( isclassifier )
err += fabs(val - val0) > FLT_EPSILON;
else
err += (val - val0)*(val - val0);
if( !resp.empty() )
resp.at<float>(i) = val;
/*if( i < 100 )
{
printf("%d. ref %.1f vs pred %.1f\n", i, val0, val);
}*/
}
if( _resp.needed() )
resp.copyTo(_resp);
return (float)(err / n * (isclassifier ? 100 : 1));
}
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void Cholesky( const Mat& A, Mat& S )
{
CV_Assert(A.type() == CV_32F);
int dim = A.rows;
S.create(dim, dim, CV_32F);
int i, j, k;
for( i = 0; i < dim; i++ )
{
for( j = 0; j < i; j++ )
S.at<float>(i,j) = 0.f;
float sum = 0.f;
for( k = 0; k < i; k++ )
{
float val = S.at<float>(k,i);
sum += val*val;
}
S.at<float>(i,i) = std::sqrt(std::max(A.at<float>(i,i) - sum, 0.f));
float ival = 1.f/S.at<float>(i, i);
for( j = i + 1; j < dim; j++ )
{
sum = 0;
for( k = 0; k < i; k++ )
sum += S.at<float>(k, i) * S.at<float>(k, j);
S.at<float>(i, j) = (A.at<float>(i, j) - sum)*ival;
}
}
}
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
average row vector, <cov> - symmetric covariation matrix */
void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples )
{
Mat mean = _mean.getMat(), cov = _cov.getMat();
int dim = (int)mean.total();
_samples.create(nsamples, dim, CV_32F);
Mat samples = _samples.getMat();
randu(samples, 0., 1.);
Mat utmat;
Cholesky(cov, utmat);
int flags = mean.cols == 1 ? 0 : GEMM_3_T;
for( int i = 0; i < nsamples; i++ )
{
Mat sample = samples.row(i);
gemm(sample, utmat, 1, mean, 1, sample, flags);
}
}
}}
/* End of file */